Private credit investing in the age of AI

Artificial intelligence has moved from concept to commercial reality with incredible speed. For investors, the question is no longer whether AI will reshape industries, but how to position their portfolios to account for the effects of AI, and where the winners and losers will emerge.

There is a lot of focus in the stock market on identifying companies that can leverage AI to accelerate growth. For credit investors and for high-net-worth clients allocating to private credit, the perspective is necessarily different. The primary concern is not who will grow the fastest, but which business models will maintain cash flow if competitive dynamics change. Resilience is more important than excitement when lending capital.

AI is already prompting a reassessment of risk across sectors. This discussion extends far beyond traditional software developers. Legal technology, consulting, insurance brokerage, real estate services and online comparison platforms are all being evaluated in a new light.

In some cases, market volatility reflects uncertainty rather than structural decline. Yet assumptions that once seemed stable now require closer scrutiny.

Private credit is at the heart of this change. Over the past decade, technology-enabled and software businesses have attracted significant private equity investment. Strong growth, high share of recurring revenue, scalable platform, and high profit margins supported the increase in valuation multiple.

Many deals were structured with leverage of 5 to 7 times Ebitda (earnings before interest, taxes, depreciation, and amortization), and in some cases leverage was even higher if revenue-based metrics such as annual recurring revenue were used to support additional borrowings.

This model was sustainable in an era of abundant liquidity and stable growth. AI introduces new variables. The risk is not that demand for software will disappear. Rather, revenue growth and profit margins in certain niches may slow as new technologies lower barriers to entry, compress prices, or shorten product life cycles. When leverage is high and equity is fully valued, even small changes in outlook can reduce equity cushions and complicate refinancing.

However, not all confusion is the same. One of the most important distinctions for credit investors is between software that is deeply embedded in mission-critical operations and products that perform more standardized tasks with limited differentiation.

Enterprise resource planning systems, billing platforms, financial management tools, and public sector management software are often tightly integrated into daily workflows. They connect multiple datasets, support regulatory compliance, and underpin reporting capabilities. Replacing them is expensive, time-consuming, and can disrupt operations. In these areas, AI is more likely to enhance existing capabilities rather than cause wholesale replacement.

In contrast, software designed primarily to automate routine human tasks, such as basic document processing, commoditized marketing tools, and certain customer service applications, may be subject to more sustained competitive pressures. As AI improves baseline capabilities and reduces development costs, pricing power can be lost. For lenders, understanding where a borrower falls on this spectrum is central to assessing long-term resilience.

The economics of AI itself add further complexity. Developing and operating sophisticated models remains capital intensive. Many providers are still refining their monetization strategies and profit pools are evolving. Rapid innovation can shorten product cycles and create uncertainty in long-term profits. For credit investors, that uncertainty must be factored into cash flow forecasts and leverage tolerances.

The ripple effects extend beyond the operating company. The expansion of AI has driven significant investment in data centers and specialized computing infrastructure. While such assets are important to the ecosystem, technological advances in chip design and computing architectures can accelerate obsolescence in ways not typically associated with traditional infrastructure sectors. For lenders with long-term exposure, the durability of assets cannot be assumed.

Against this backdrop, underwriting discipline becomes even more important. For private credit allocation within an asset portfolio, leverage should be set based on realistic and sustainable cash flow projections, rather than ambitious growth projections. A structure built on optimistic assumptions leaves little room for error if competitive dynamics change.

Equity buffers are equally important. Significant sponsor capital at risk provides protection during periods of volatility and supports adjustments through refinancing cycles. Diversification also plays a role. Concentrated exposure to a single subsector experiencing rapid technological change can amplify downside risks.

Qualitative judgment is equally important. How central is this product to the customer’s daily operations? What are the financial and operational switching costs? Is management actively integrating AI tools to enhance service? Or is the business vulnerable to displacement? These questions are increasingly at the heart of credit analysis.

It’s also important to recognize that AI can enhance your credit profile. Many companies are implementing AI to streamline internal processes, improve efficiency, and enhance customer engagement. If increased productivity leads to higher profit margins and more predictable free cash flow, your ability to service your debt may improve. The challenge lies in distinguishing between structural benefits and short-term aspirations.

For wealth investors, private credit continues to offer attractive income and diversification features. However, this asset class does not exist outside of broader technological changes. As AI reshapes the competitive landscape, well-known indicators of stability – recurring revenue, historical growth trends, and high profit margins – will require deeper consideration.

For credit investors, the objective is not to predict technological winners, but rather to understand how innovation interacts with leverage, capital structure, and cash flow durability. In times of rapid change, careful structuring, a well-diversified portfolio, moderate leverage, and disciplined analysis are the most reliable foundations for generating consistent returns.

Giampaolo Pellegrini is co-head of the parallel financing department. Andrew Tan is Chief Executive Officer of Apac and Head of Apac Private Credit, Muzinich & Co.